Build 20 Real World Data Science & Machine Learning Projects

Traditionally, humans have analyzed data and adapted systems to the changes in data patterns. However, as the volume of data surpasses the ability for humans to make sense of it and manually write rules, we will turn increasingly to automated systems that can learn from the data, and, importantly, changes in data, to adapt to a shifting data landscape.

Machine Learning is already everywhere

We see machine learning all around us in the products we use today, but it isn t always apparent to us that machine learning is behind it all. While tagging objects and people in pictures is clearly machine learning, you may not realize that features like video recommendation systems are also often powered by machine learning.

Of course, perhaps the biggest example of all is Google Search. Every time you use Google Search, you are using a system that has many machine learning systems at its core, from understanding the text of your query to adjusting the results based on your personal interests. When you search for Java , machine learning determines which results to show first, depending on whether it thinks you are a coffee expert or a developer. Perhaps you re both!

Today, machine learning s immediate applications are already quite wide–ranging, including image recognition, fraud detection, recommendation engines, as well as text and speech systems. These powerful capabilities can be applied to a wide range of fields, from diabetic retinopathy and skin cancer detection to retail, and of course transportation, in the form of self–parking and self–driving vehicles.